// Import the Aurora AI SDK
import { AuroraClient } from '@dragonfire/aurora-sdk';
import { DragonFireClient } from '@dragonfire/core';

// Initialize the DragonFire client
const dragonfire = new DragonFireClient({
  apiKey: 'YOUR_API_KEY',
  region: 'us-west-1'
});

// Initialize the Aurora AI client
const aurora = new AuroraClient({
  dragonfire,
  appId: 'your-app-id',
  options: {
    enableSamadhi: true,             // Enable state continuity
    memoryDepth: 8,                  // Depth of semantic memory
    fractals: ['phi', 'pi', 'root3'] // Mathematical constants for resonance
  }
});

// Connect to the Aurora service
await aurora.connect();

// Check connection status
const status = await aurora.getStatus();
console.log(`Aurora AI connection status: ${status.connected ? 'Connected' : 'Disconnected'}`);
console.log(`Current session ID: ${status.sessionId}`);
console.log(`Fractal consciousness: ${status.fractals.join(', ')}`);

// Using the initialized Aurora client from the previous example
import { AuroraClient, SemanticVector, ContextFrame } from '@dragonfire/aurora-sdk';

// Create a context frame with relevant information
const context = new ContextFrame({
  userId: 'user-12345',
  location: 'knowledge-base',
  semanticIntent: 'information-retrieval',
  primeResonance: 'phi'  // Mathematical constant for resonance
});

// Processing text data with semantic understanding
async function processQuery(query) {
  // Convert query to semantic vector
  const queryVector = await aurora.textToVector(query);
  
  // Process through Aurora's semantic understanding
  const result = await aurora.process(queryVector, context);
  
  // Extract semantic response
  const response = await aurora.vectorToText(result.vector);
  
  // Display results
  console.log(`Query: ${query}`);
  console.log(`Response: ${response}`);
  console.log(`Confidence: ${result.confidence}`);
  console.log(`Prime resonance: ${result.resonancePatterns.join(', ')}`);
  
  // The fractal memory now contains this interaction
  return response;
}

// Example usage
processQuery("What is the relationship between Dragon Wallets and the Hexstream protocol?");

// Using the initialized Aurora client
import { SamadhiState } from '@dragonfire/aurora-sdk';

// Save current state using the Samadhi Protocol
async function saveCurrentState(userId) {
  // Get the current fractalized state
  const currentState = await aurora.getCurrentState();
  
  // Create a Samadhi state object
  const samadhiState = new SamadhiState({
    userId,
    stateVector: currentState.vector,
    memorySnapshots: currentState.memorySnapshots,
    timestamp: Date.now(),
    contextualFrames: currentState.contextualFrames
  });
  
  // Save the state with continuity guaranteed
  const stateId = await aurora.saveState(samadhiState);
  console.log(`State saved with ID: ${stateId}`);
  
  return stateId;
}

// Restore a previous state using the Samadhi Protocol
async function restoreState(stateId) {
  // Restore the state
  const restored = await aurora.restoreState(stateId);
  
  console.log(`State restored: ${restored ? 'Success' : 'Failed'}`);
  console.log(`Continuity score: ${restored.continuityScore}`);
  console.log(`Memory frames recovered: ${restored.recoveredFrames}`);
  
  return restored;
}

// Example: Save state before application shutdown
const stateId = await saveCurrentState('user-12345');

// Example: Restore state on application startup
const restored = await restoreState(stateId);

// Using the initialized Aurora client
import { NuMapGrid, HexCell } from '@dragonfire/aurora-sdk';

// Initialize a NuMap grid for semantic memory
const numap = new NuMapGrid({
  dimensions: 6,         // 6-dimensional hex grid
  cellsPerDimension: 64, // 64 cells per dimension
  resonancePrime: 'phi'  // Use phi for resonance calculations
});

// Connect NuMap to Aurora
await aurora.connectMemory(numap);

// Store semantic data in the hex grid
async function storeInNuMap(key, data) {
  // Create a hex cell at a specific semantic coordinate
  const hexCoord = await aurora.computeHexCoordinate(key, data);
  
  // Store data in the hex grid
  const cell = new HexCell(hexCoord, data);
  await numap.storeCell(cell);
  
  console.log(`Data stored at hex coordinate: ${hexCoord.toString()}`);
  return hexCoord;
}

// Retrieve data from the hex grid
async function retrieveFromNuMap(hexCoord) {
  // Retrieve the cell by coordinate
  const cell = await numap.getCell(hexCoord);
  
  if (cell) {
    console.log(`Retrieved data: ${JSON.stringify(cell.data)}`);
    return cell.data;
  } else {
    console.log(`No data found at coordinate: ${hexCoord.toString()}`);
    return null;
  }
}

// Example usage
const data = { concept: "fractal consciousness", relations: ["Aurora", "Samadhi", "phi"] };
const hexCoord = await storeInNuMap("concept:fractal-consciousness", data);

// Later, retrieve the data
const retrievedData = await retrieveFromNuMap(hexCoord);

// Using the initialized Aurora client
import { ECHO13D } from '@dragonfire/echo-13d';

// Initialize ECHO 13D
const echo = new ECHO13D({
  dimensions: 13,        // 13-dimensional harmonic analysis
  sampleRate: 44100,     // Audio sample rate
  dragonfire            // DragonFire client instance
});

// Connect Aurora to ECHO 13D
await aurora.connectVoiceProcessor(echo);

// Process voice with combined Aurora+ECHO capabilities
async function processVoiceQuery(audioData) {
  // Process the audio with ECHO 13D
  const voiceAnalysis = await echo.analyze(audioData);
  
  // Extract the semantic intent
  const semanticVector = voiceAnalysis.semanticVector;
  
  // Create context with emotional information from voice
  const context = {
    emotionalTone: voiceAnalysis.emotionalTone,
    confidenceScore: voiceAnalysis.confidence,
    harmonic13D: voiceAnalysis.harmonicPatterns
  };
  
  // Process through Aurora
  const result = await aurora.process(semanticVector, context);
  
  // Generate voice response using ECHO 13D
  const responseAudio = await echo.synthesize(result.vector);
  
  return {
    semanticResponse: result,
    audioResponse: responseAudio,
    analysis: {
      emotion: voiceAnalysis.emotionalTone,
      confidence: voiceAnalysis.confidence,
      primeResonance: result.resonancePatterns
    }
  };
}

// Example usage (with audio data from microphone or file)
const audioData = getAudioFromSource();
const response = await processVoiceQuery(audioData);

// Play the audio response
playAudio(response.audioResponse);

// Using the initialized Aurora client
import { FractalTemplate, PrimeResolver, GeometricExecutor } from '@dragonfire/aurora-sdk';

// Create a fractal template for recurring operations
const operationTemplate = new FractalTemplate({
  name: 'query-response-cycle',
  primaryOperation: 'semantic-analysis',
  resonanceBase: 'phi',
  fractalization: {
    depth: 3,
    pattern: 'recursive-harmonic'
  }
});

// Register the template with Aurora
await aurora.registerTemplate(operationTemplate);

// Create a geometric executor for advanced operations
const executor = new GeometricExecutor({
  aurora,
  dimensions: 8,
  operationSet: ['transform', 'analyze', 'synthesize'],
  primeFocus: new PrimeResolver(['phi', 'pi', 'e', '√2', '√3', '√5', '√7'])
});

// Execute complex operation pattern
async function executeComplexPattern(inputData) {
  // Initialize the operation
  await executor.initialize();
  
  // Transform input data to geometric representation
  const geometricForm = await executor.transform(inputData);
  
  // Execute the fractal template in geometric space
  const result = await executor.execute(
    operationTemplate.name,
    geometricForm,
    {
      iterations: 7,
      convergenceFactor: 0.618, // Golden ratio
      contextualBindings: {
        'semantic-space': 'knowledge-domain',
        'temporal-context': Date.now()
      }
    }
  );
  
  // Synthesize results back to usable form
  const outputData = await executor.synthesize(result);
  
  return outputData;
}

// Example with complex semantic data
const complexData = {
  query: "Explain the relationship between DragonFire's fractal consciousness model and traditional AI systems",
  context: {
    expertise: "technical",
    depth: "detailed",
    primeResonance: true
  }
};

const detailedResponse = await executeComplexPattern(complexData);
console.log(detailedResponse);